Add SetFit model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,266 @@
+---
+base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Aquest text és Varis
+- text: Aquest text és Mobiliari Urbà
+- text: Aquest text és Velocitat
+- text: Aquest text és Parcs i Jardins
+- text: Aquest text és Enllumenat
+inference: true
+---
+
+# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 14 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                             |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Aquest text és Arbrat'</li><li>'Aquest text és Arbrat'</li><li>'Aquest text és Arbrat'</li></ul>                            |
+| 1     | <ul><li>'Aquest text és Circulació'</li><li>'Aquest text és Circulació'</li><li>'Aquest text és Circulació'</li></ul>                |
+| 2     | <ul><li>'Aquest text és Comentaris'</li><li>'Aquest text és Comentaris'</li><li>'Aquest text és Comentaris'</li></ul>                |
+| 3     | <ul><li>'Aquest text és Enllumenat'</li><li>'Aquest text és Enllumenat'</li><li>'Aquest text és Enllumenat'</li></ul>                |
+| 4     | <ul><li>'Aquest text és Informació'</li><li>'Aquest text és Informació'</li><li>'Aquest text és Informació'</li></ul>                |
+| 5     | <ul><li>'Aquest text és Manteniment'</li><li>'Aquest text és Manteniment'</li><li>'Aquest text és Manteniment'</li></ul>             |
+| 6     | <ul><li>'Aquest text és Mobiliari Urbà'</li><li>'Aquest text és Mobiliari Urbà'</li><li>'Aquest text és Mobiliari Urbà'</li></ul>    |
+| 7     | <ul><li>'Aquest text és Neteja'</li><li>'Aquest text és Neteja'</li><li>'Aquest text és Neteja'</li></ul>                            |
+| 8     | <ul><li>'Aquest text és Parcs i Jardins'</li><li>'Aquest text és Parcs i Jardins'</li><li>'Aquest text és Parcs i Jardins'</li></ul> |
+| 9     | <ul><li>'Aquest text és Senyalització'</li><li>'Aquest text és Senyalització'</li><li>'Aquest text és Senyalització'</li></ul>       |
+| 10    | <ul><li>'Aquest text és Sorolls'</li><li>'Aquest text és Sorolls'</li><li>'Aquest text és Sorolls'</li></ul>                         |
+| 11    | <ul><li>'Aquest text és Suggeriments'</li><li>'Aquest text és Suggeriments'</li><li>'Aquest text és Suggeriments'</li></ul>          |
+| 12    | <ul><li>'Aquest text és Varis'</li><li>'Aquest text és Varis'</li><li>'Aquest text és Varis'</li></ul>                               |
+| 13    | <ul><li>'Aquest text és Velocitat'</li><li>'Aquest text és Velocitat'</li><li>'Aquest text és Velocitat'</li></ul>                   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("adriansanz/setfitemotions")
+# Run inference
+preds = model("Aquest text és Varis")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median | Max |
+|:-------------|:----|:-------|:----|
+| Word count   | 4   | 4.2143 | 6   |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 10                    |
+| 1     | 10                    |
+| 2     | 10                    |
+| 3     | 10                    |
+| 4     | 10                    |
+| 5     | 10                    |
+| 6     | 10                    |
+| 7     | 10                    |
+| 8     | 10                    |
+| 9     | 10                    |
+| 10    | 10                    |
+| 11    | 10                    |
+| 12    | 10                    |
+| 13    | 10                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0009 | 1    | 0.2021        | -               |
+| 0.0439 | 50   | 0.0263        | -               |
+| 0.0879 | 100  | 0.0032        | -               |
+| 0.1318 | 150  | 0.0015        | -               |
+| 0.1757 | 200  | 0.0012        | -               |
+| 0.2197 | 250  | 0.0007        | -               |
+| 0.2636 | 300  | 0.0008        | -               |
+| 0.3076 | 350  | 0.0006        | -               |
+| 0.3515 | 400  | 0.0003        | -               |
+| 0.3954 | 450  | 0.0003        | -               |
+| 0.4394 | 500  | 0.0004        | -               |
+| 0.4833 | 550  | 0.0005        | -               |
+| 0.5272 | 600  | 0.0004        | -               |
+| 0.5712 | 650  | 0.0005        | -               |
+| 0.6151 | 700  | 0.0005        | -               |
+| 0.6591 | 750  | 0.0002        | -               |
+| 0.7030 | 800  | 0.0001        | -               |
+| 0.7469 | 850  | 0.0004        | -               |
+| 0.7909 | 900  | 0.0002        | -               |
+| 0.8348 | 950  | 0.0003        | -               |
+| 0.8787 | 1000 | 0.0002        | -               |
+| 0.9227 | 1050 | 0.0002        | -               |
+| 0.9666 | 1100 | 0.0003        | -               |
+| 1.0105 | 1150 | 0.0002        | -               |
+| 1.0545 | 1200 | 0.0002        | -               |
+| 1.0984 | 1250 | 0.0002        | -               |
+| 1.1424 | 1300 | 0.0003        | -               |
+| 1.1863 | 1350 | 0.0003        | -               |
+| 1.2302 | 1400 | 0.0001        | -               |
+| 1.2742 | 1450 | 0.0002        | -               |
+| 1.3181 | 1500 | 0.0001        | -               |
+| 1.3620 | 1550 | 0.0001        | -               |
+| 1.4060 | 1600 | 0.0003        | -               |
+| 1.4499 | 1650 | 0.0001        | -               |
+| 1.4938 | 1700 | 0.0001        | -               |
+| 1.5378 | 1750 | 0.0001        | -               |
+| 1.5817 | 1800 | 0.0001        | -               |
+| 1.6257 | 1850 | 0.0001        | -               |
+| 1.6696 | 1900 | 0.0001        | -               |
+| 1.7135 | 1950 | 0.0001        | -               |
+| 1.7575 | 2000 | 0.0002        | -               |
+| 1.8014 | 2050 | 0.0001        | -               |
+| 1.8453 | 2100 | 0.0001        | -               |
+| 1.8893 | 2150 | 0.0002        | -               |
+| 1.9332 | 2200 | 0.0001        | -               |
+| 1.9772 | 2250 | 0.0002        | -               |
+| 2.0211 | 2300 | 0.0001        | -               |
+| 2.0650 | 2350 | 0.0001        | -               |
+| 2.1090 | 2400 | 0.0001        | -               |
+| 2.1529 | 2450 | 0.0001        | -               |
+| 2.1968 | 2500 | 0.0001        | -               |
+| 2.2408 | 2550 | 0.0001        | -               |
+| 2.2847 | 2600 | 0.0           | -               |
+| 2.3286 | 2650 | 0.0001        | -               |
+| 2.3726 | 2700 | 0.0001        | -               |
+| 2.4165 | 2750 | 0.0001        | -               |
+| 2.4605 | 2800 | 0.0001        | -               |
+| 2.5044 | 2850 | 0.0001        | -               |
+| 2.5483 | 2900 | 0.0001        | -               |
+| 2.5923 | 2950 | 0.0001        | -               |
+| 2.6362 | 3000 | 0.0001        | -               |
+| 2.6801 | 3050 | 0.0001        | -               |
+| 2.7241 | 3100 | 0.0001        | -               |
+| 2.7680 | 3150 | 0.0001        | -               |
+| 2.8120 | 3200 | 0.0001        | -               |
+| 2.8559 | 3250 | 0.0001        | -               |
+| 2.8998 | 3300 | 0.0001        | -               |
+| 2.9438 | 3350 | 0.0001        | -               |
+| 2.9877 | 3400 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 12,
+  "output_past": true,
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+  "torch_dtype": "float32",
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+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
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+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false,
+  "labels": null
+}

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modules.json

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sentence_bert_config.json

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+  "do_lower_case": false
+}

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+}